






import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								__magic_name__		:Optional[Any]   =   flax_key_tuple[:-1] + ('''weight''',)
								__magic_name__		:Tuple   =   torch.permute(snake_case,	(0, 2, 1)			)
				elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case			):
								# linear layer
								__magic_name__		:Dict   =   flax_key_tuple[:-1] + ('''weight''',)
								__magic_name__		:Union[str, Any]   =   flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								__magic_name__		:List[str]   =   flax_key_tuple[:-1] + ('''weight''',)

				return flax_key_tuple, flax_tensor

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				if "metadata" in layer:
								__magic_name__		:List[str]   =   layer.split('''metadata'''			)
								__magic_name__		:int   =   ''''''.join(split_layer[0]			)[:-1]
								__magic_name__		:Optional[Any]   =   [tuple(('''metadata''' + split_layer[1]).split('''/'''			)			)]
				elif "kvstore" in layer:
								__magic_name__		:Union[str, Any]   =   layer.split('''kvstore'''			)
								__magic_name__		:int   =   ''''''.join(split_layer[0]			)[:-1]
								__magic_name__		:List[str]   =   [tuple(('''kvstore''' + split_layer[1]).split('''/'''			)			)]

				else:
								__magic_name__		:Dict   =   layer.split('''/'''			)
								__magic_name__		:Union[str, Any]   =   '''/'''.join(split_layer[:-1]			)
								__magic_name__		:Dict   =   (split_layer[-1],)

				if "kvstore/path" in layer:
								__magic_name__		:Optional[Any]   =   f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
				elif "kvstore/driver" in layer:
								__magic_name__		:Optional[Any]   =   '''file'''
				else:
								__magic_name__		:Union[str, Any]   =   checkpoint_info[layer]

				return curr_real_layer_name, split_layer, content

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   rename_keys(snake_case			)
				__magic_name__		:List[str]   =   {}
				for k, v in current_block.items():
								__magic_name__		:Union[str, Any]   =   v
				__magic_name__		:List[str]   =   new_current_block
				torch.save(snake_case,	snake_case			)

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case,	snake_case = WEIGHTS_NAME			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   convert_file_size_to_int(snake_case			)
				__magic_name__		:Union[str, Any]   =   []
				__magic_name__		:Optional[Any]   =   {}
				__magic_name__		:Optional[int]   =   0
				__magic_name__		:Optional[int]   =   0

				os.makedirs(snake_case,	exist_ok=snake_case			)
				with gfile.GFile(switch_checkpoint_path + '''/checkpoint''',	'''rb'''			) as fp:
								__magic_name__		:List[Any]   =   serialization.msgpack_restore(fp.read()			)['''optimizer''']['''target''']
								__magic_name__		:List[Any]   =   flatten_dict(snake_case,	sep='''/'''			)

				__magic_name__		:Any   =   {}
				for layer in checkpoint_info.keys():
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Optional[Any]   =   get_key_and_tensorstore_dict(
								    snake_case,	snake_case,	snake_case			)
								if curr_real_layer_name in all_layers:
												__magic_name__		:str   =   content
								else:
												__magic_name__		:Union[str, Any]   =   {split_layer[-1]: content}

				for key in all_layers.keys():
								# open tensorstore file
								__magic_name__		:Any   =   ts.open(unflatten_dict(all_layers[key]			)			).result().read().result()
								__magic_name__		:str   =   torch.tensor(snake_case			)
								__magic_name__		:List[str]   =   raw_weights.numel() * dtype_byte_size(raw_weights.dtype			)

								# use the renaming pattern from the small conversion scripts
								__magic_name__  ,					__magic_name__		:Optional[Any]   =   rename_base_flax_keys(tuple(key.split('''/'''			)			),	snake_case			)
								__magic_name__		:Optional[Any]   =   '''/'''.join(snake_case			)

								# If this weight is going to tip up over the maximal size, we split.
								if current_block_size + weight_size > max_shard_size:
												__magic_name__		:Union[str, Any]   =   os.path.join(
												    snake_case,	weights_name.replace('''.bin''',	f'''-{len(snake_case			)+1:05d}-of-???.bin'''			)			)
												rename_and_save_block(snake_case,	snake_case			)
												sharded_state_dicts.append(current_block.keys()			)
												del current_block
												__magic_name__		:Union[str, Any]   =   {}
												__magic_name__		:List[str]   =   0

								__magic_name__		:int   =   raw_weights.to(getattr(snake_case,	snake_case			)			)
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				__magic_name__		:int   =   os.path.join(snake_case,	weights_name.replace('''.bin''',	f'''-{len(snake_case			)+1:05d}-of-???.bin'''			)			)
				rename_and_save_block(snake_case,	snake_case			)
				sharded_state_dicts.append(current_block.keys()			)

				# If we only have one shard, we return it
				if len(snake_case			) == 1:
								return {weights_name: sharded_state_dicts[0]}, None

				# Otherwise, let's build the index
				__magic_name__		:Union[str, Any]   =   {}
				__magic_name__		:Union[str, Any]   =   {}
				for idx, shard in enumerate(snake_case			):
								__magic_name__		:Union[str, Any]   =   weights_name.replace(
								    '''.bin''',	f'''-{idx+1:05d}-of-{len(snake_case			):05d}.bin'''			)  # len(sharded_state_dicts):05d}
								__magic_name__		:Dict   =   os.path.join(snake_case,	weights_name.replace('''.bin''',	f'''-{idx+1:05d}-of-???.bin'''			)			)
								os.rename(snake_case,	os.path.join(snake_case,	snake_case			)			)
								__magic_name__		:str   =   shard
								for key in shard:
												__magic_name__		:List[str]   =   shard_file

    # Add the metadata
				__magic_name__		:List[Any]   =   {'''total_size''': total_size}
				__magic_name__		:int   =   {'''metadata''': metadata, '''weight_map''': weight_map}

				with open(os.path.join(snake_case,	snake_case			),	'''w''',	encoding='''utf-8'''			) as f:
								__magic_name__		:Any   =   json.dumps(snake_case,	indent=2,	sort_keys=snake_case			) + '''\n'''
								f.write(snake_case			)

				return metadata, index


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      str				=		argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--switch_t5x_checkpoint_path""",
					    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
					    type=str,
					    required=False,
					    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
					)
					parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
					parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
					    type=str,
					    required=False,
					    help="""Path to the output pytorch model.""",
					)
					SCREAMING_SNAKE_CASE__   :      Optional[int]				=		parser.parse_args()
					shard_on_the_fly(
					    args.switch_tax_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.max_shard_size,
					    args.dtype,
					)

def   __lowercase       (			):







				"""simple docstring"""






				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

				__magic_name__		:int   =   SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''			)
				config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''			)
				__magic_name__		:List[Any]   =   SwitchTransformersForConditionalGeneration.from_pretrained(
				    '''/home/arthur_huggingface_co/transformers/switch_converted''',	device_map='''auto'''			)

				__magic_name__		:int   =   TaTokenizer.from_pretrained('''t5-small'''			)
				__magic_name__		:List[Any]   =   '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

				__magic_name__		:Optional[Any]   =   tokenizer(snake_case,	return_tensors='''pt'''			).input_ids
				__magic_name__		:Any   =   model.generate(snake_case,	decoder_start_token_id=0			)
				print(tokenizer.decode(out[0]			)			)














"""simple docstring"""
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case : Optional[Any]     		=		"""."""

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
snake_case : List[str]     		=		[
    """Assert""",
    """AssignVariableOp""",
    """EmptyTensorList""",
    """MergeV2Checkpoints""",
    """ReadVariableOp""",
    """ResourceGather""",
    """RestoreV2""",
    """SaveV2""",
    """ShardedFilename""",
    """StatefulPartitionedCall""",
    """StaticRegexFullMatch""",
    """VarHandleOp""",
]


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: Tuple ,     __snake_case: Any  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    SavedModel()
       __magic_name__      				=    []

       with open(os.path.join(__snake_case ,     'utils' ,     'tf_ops' ,     'onnx.json'  )  ) as f:
              __magic_name__      				=    json.load(__snake_case  )['opsets']

       for i in range(1 ,     opset + 1  ):
              onnx_ops.extend(onnx_opsets[str(__snake_case  )]  )

       with open(__snake_case ,     'rb'  ) as f:
              saved_model.ParseFromString(f.read()  )

       __magic_name__      				=    set()

       # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
       for meta_graph in saved_model.meta_graphs:
              # Add operations in the graph definition
              model_op_names.update(node.op for node in meta_graph.graph_def.node  )

              # Go through the functions in the graph definition
              for func in meta_graph.graph_def.library.function:
                     # Add operations in each function
                     model_op_names.update(node.op for node in func.node_def  )

    # Convert to list, sorted if you want
       __magic_name__      				=    sorted(__snake_case  )
       __magic_name__      				=    []

       for op in model_op_names:
              if op not in onnx_ops and op not in INTERNAL_OPS:
                     incompatible_ops.append(__snake_case  )

       if strict and len(__snake_case  ) > 0:
              raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops  )
       elif len(__snake_case  ) > 0:
              print(F"""Found the following incompatible ops for the opset {opset}:"""  )
              print(*__snake_case ,     sep='\n'  )
       else:
              print(F"""The saved model {saved_model_path} can properly be converted with ONNX."""  )


if __name__ == "__main__":
 snake_case : Optional[Any]     		=		argparse.ArgumentParser()
 parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
 parser.add_argument(
     """--opset""", default=1_2, type=int, help="""The ONNX opset against which the model has to be tested."""
 )
 parser.add_argument(
     """--framework""", choices=["""onnx"""], default="""onnx""", help="""Frameworks against which to test the saved model."""
 )
 parser.add_argument(
     """--strict""", action="""store_true""", help="""Whether make the checking strict (raise errors) or not (raise warnings)"""
 )
 snake_case : Any     		=		parser.parse_args()

 if args.framework == "onnx":
  onnx_compliancy(args.saved_model_path, args.strict, args.opset)